Gene-Environment Interactions in Human Evolution and Complex Traits

项目摘要(未更改) 基因变异、环境暴露及其相互作用是所有人类疾病的病因学基础。 虽然全基因组关联研究揭示了许多与性状相关的遗传变异和 流行病学研究已经明确了无数与疾病相关的环境因素，它们的作用 交互作用的研究要少得多，主要是因为缺乏非常大的人口队列，高质量 环境措施和有效工具。这项提议旨在描述基因与环境之间的相互作用。 (GEI)在人类进化和复杂性状上。遗传和多基因适应当地环境 在人类进化过程中形成了基因与环境的关系和复杂的遗传结构 特征。利用越来越多的古代DNA，我们将首先开发和应用统计测试来识别 对农业革命的遗传和多基因反应。适应性遗传变异的发现和 多基因特征将有助于我们理解和研究当前复杂疾病的流行，这些疾病是 可能是当今基因和生活方式不匹配的结果。第二，直接识别和量化复合体中的GEI 特征，我们将开发一个高效的计算管道，并跨 基因组、表现组和英国生物库中精选的高质量环境因素。所有汇总统计数据将 作为数据库在专门的网站上公开发布，以推动进一步的探索，如元分析 以及测试在队列和祖先之间的可复制性。第三，为了协助和指导未来的全球环境倡议研究，我们将 开发首个用于全基因组相互作用研究的生物信息学工具(PheWIS) 环境暴露，使得能够有效和公正地寻找环境可改变的表型效应。 此外，为了减轻GEI研究中大量暴露和临床的多重测试负担 结果，我们将检查孟德尔随机化分析是否与全表型关联研究相结合 (Phewas-MR)可能是确定潜在因果暴露-结果关系的优先顺序的有效方式，这 可以增加检测GEI的统计能力，并有助于下游功能机制的寻找。 最后，为了改善多基因评分(PGS)在祖先和子组之间的可移植性， 同样，我们将使用模拟和经验数据来测试GEI的显式统计建模是否可以缓解 问题就在这里。同时，我们将研究PGS-环境交互作用分析是否是一种有效的方法 确定可降低遗传风险的可操作环境暴露。总的来说，这项拟议的研究将 生成新的方法、计算工具、数据库资源和对通用模式的新见解 人类复杂性状中的遗传基因。

Project Summary (Unchanged) Genetic variations, environmental exposures, and their interactions underlie the etiology of all human diseases. While genome-wide association studies have revealed many trait-associated genetic variants and epidemiological studies have pinpointed myriad disease-associated environmental factors, the role of their interactions is much less explored, mainly due to the lack of very large population cohorts, high-quality environmental measures, and efficient tools. This proposal aims to characterize gene-environment interactions (GEI) in both human evolution and complex traits. Genetic and polygenic adaptations to local environments during human evolution have shaped the gene-environment relationship and the genetic architecture of complex traits. Leveraging the growing number of ancient DNA, we will first develop and apply statistical tests to identify genetic and polygenic responses to the Agricultural Revolution. The findings of adaptive genetic variants and polygenic traits will inform our understanding and study of the current epidemics of complex diseases, which are likely results of present-day gene-lifestyle mismatches. Second, to directly identify and quantify GEI in complex traits, we will develop an efficient computational pipeline and perform large-scale interaction analysis across the genome, phenome, and selected high-quality environmental factors in UK Biobank. All summary statistics will be released publicly as a database on a dedicated website to fuel further explorations, such as meta-analysis and testing for replicability across cohorts and ancestries. Third, to assist and guide future GEI studies, we will develop the first bioinformatics tool for phenome-wide interaction study (PheWIS) of target genetic variants and environmental exposures, enabling efficient and unbiased search for environment-modifiable phenotypic effects. Moreover, to alleviate the multiple testing burden in GEI studies with a large number of exposures and clinical outcomes, we will examine if Mendelian randomization analysis coupled with phenome-wide association study (PheWAS-MR) could be an effective way to prioritize potentially causal exposure-outcome relationships, which may increase the statistical power of detecting GEI and assist the downstream search for functional mechanisms. Lastly, as an effort to improve the portability of polygenic score (PGS) across ancestries and subgroups with the same ancestry, we will use simulated and empirical data to test if explicit statistical modeling of GEI could mitigate the problem. Concurrently, we will examine if PGS-environment interaction analysis is an effective approach to identify actionable environmental exposures that attenuate genetic risks. Overall, this proposed research will generate new methods, computational tools, database resources, and novel insights into the general patterns of GEI in human complex traits.